Evaluation results for RERobbins/qg_T5_nq model as a base model for other tasks
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eladven
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README.md
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# RERobbins/qg_T5_nq model
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This model is based on google/t5-v1_1-base pretrained model.
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## Model Recycling
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=8.37&mnli_lp=nan&20_newsgroup=4.19&ag_news=1.52&amazon_reviews_multi=-0.13&anli=13.06&boolq=12.35&cb=30.27&cola=9.40&copa=8.50&dbpedia=6.63&esnli=5.31&financial_phrasebank=20.66&imdb=0.80&isear=2.61&mnli=11.88&mrpc=14.91&multirc=5.37&poem_sentiment=16.54&qnli=3.67&qqp=4.70&rotten_tomatoes=3.64&rte=14.87&sst2=0.55&sst_5bins=4.76&stsb=18.60&trec_coarse=4.75&trec_fine=9.93&tweet_ev_emoji=13.56&tweet_ev_emotion=6.59&tweet_ev_hate=2.08&tweet_ev_irony=9.67&tweet_ev_offensive=2.04&tweet_ev_sentiment=1.56&wic=13.60&wnli=6.62&wsc=12.26&yahoo_answers=4.11&model_name=RERobbins%2Fqg_T5_nq&base_name=google%2Ft5-v1_1-base) using RERobbins/qg_T5_nq as a base model yields average score of 77.20 in comparison to 68.82 by google/t5-v1_1-base.
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The model is ranked 2nd among all tested models for the google/t5-v1_1-base architecture as of 18/01/2023
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Results:
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| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers |
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|---------------:|----------:|-----------------------:|-------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
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| 87.0685 | 89.7 | 66.78 | 51.125 | 77.9205 | 85.7143 | 79.5781 | 49 | 77.4 | 90.8897 | 87.4 | 93.788 | 73.6636 | 87.3881 | 87.7451 | 61.5099 | 84.6154 | 93.0441 | 88.2958 | 89.6811 | 75.4513 | 94.2661 | 56.6063 | 87.3921 | 98 | 92 | 47.02 | 82.1956 | 53.6027 | 77.2959 | 84.6512 | 71.4425 | 69.4357 | 53.5211 | 60.5769 | 73.3667 |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
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